system

Abstract

The system according to the embodiment aims to efficiently share, evaluate, and search prompts for generative AI. [Solution] A system according to an embodiment includes a sharing unit, an evaluation unit, a search unit, and a monitoring unit. The sharing unit shares prompts. The evaluation unit evaluates the prompts shared by the sharing unit. The search unit searches for prompts evaluated by the evaluation unit. The monitoring unit monitors the usage status of the prompts searched for by the search unit.

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to properly share, evaluate, and search prompts for generative AI, and it was time-consuming to find the prompts that would produce the desired output.

[0005] The system according to the embodiment aims to efficiently share, evaluate, and search prompts for generative AI. [Means for solving the problem]

[0006] The system according to the embodiment includes a sharing unit, an evaluation unit, a search unit, and a monitoring unit. The sharing unit shares prompts. The evaluation unit evaluates the prompts shared by the sharing unit. The search unit searches for the prompts evaluated by the evaluation unit. The monitoring unit monitors the usage status of the prompts searched for by the search unit. [Effects of the Invention]

[0007] An embodiment of the system allows for efficient sharing, evaluation, and retrieval of prompts for generative AI. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The prompt sharing system according to an embodiment of the present invention is a system that open-sources prompts for use with a generative AI and provides a community-based service. This prompt sharing system provides value to both prompt creators and users. Specifically, it includes a "sharing section" for sharing prompts, allowing users to upload their own prompts and share them with other users. It also includes an "evaluation section" for rating prompts, allowing users to rate shared prompts and evaluate their quality. It also includes a "search section" for searching prompts, allowing users to easily find prompts that meet their needs. Finally, it includes a "monitoring section" for monitoring the usage of prompts, allowing users to continuously evaluate and improve the usability of prompts. This system allows prompt creators to share their prompts and receive evaluations to identify areas for improvement, and users can easily find prompts that meet their needs and achieve the output they desire. For example, the prompt sharing system allows users to upload prompts they create and share them with other users. For example, by sharing a specific prompt for a generative AI, other users can use that prompt. Next, the prompt sharing system allows users to evaluate the shared prompts and evaluate their quality. For example, by rating the prompt's usefulness and accuracy, other users can use the prompts as a reference. Furthermore, the prompt sharing system makes it easy for users to find prompts that suit their purposes. For example, they can find prompts that suit their purposes by searching for prompts for a specific generation AI. Finally, the prompt sharing system can continuously evaluate the usage of prompts and identify areas for improvement. For example, it can identify popular prompts by monitoring which prompts are used most frequently. This mechanism allows prompt creators to share their prompts and receive evaluations to identify areas for improvement, and users can easily find prompts that suit their purposes and obtain the output they expect.This allows the prompt sharing system to efficiently share, evaluate, search, and monitor prompts.

[0029] A prompt sharing system according to an embodiment includes a sharing unit, an evaluation unit, a search unit, and a monitoring unit. The sharing unit allows users to upload prompts they have created and share them with other users. For example, the sharing unit allows users to upload their own prompts in file format, allowing other users to download and use the prompts. The sharing unit can also provide an interface for inputting prompt metadata (e.g., prompt titles, descriptions, tags, etc.). For example, the sharing unit allows users to input prompt titles and descriptions, making it easier for other users to understand the content of the prompt. The sharing unit can also provide a version management function for prompts. For example, the sharing unit allows users to upload new versions of prompts and allow other users to compare them with previous versions. The evaluation unit allows users to evaluate shared prompts and evaluate their quality. For example, the evaluation unit provides an evaluation form that allows users to evaluate the usefulness and accuracy of prompts. The evaluation unit can also provide a function that allows users to leave comments on prompts. For example, the evaluation unit allows users to input comments about improvements to the prompts and their usability, allowing other users to refer to them. The evaluation unit can also compile the prompt evaluation results and display them in a ranking format. For example, the evaluation unit displays the most highly rated prompts in a ranked format so that other users can refer to them. The search unit allows users to search for prompts that suit their purposes. For example, the search unit provides an interface that allows users to search by entering the title, description, tags, etc. of a prompt. The search unit can also provide a filtering function. For example, the search unit provides filtering options that allow users to search for prompts for a specific generation AI. Furthermore, the search unit can display search results in a ranked format. For example, the search unit displays the most highly rated prompts at the top of the search results so that users can easily find prompts that suit their purposes. The monitoring unit continuously evaluates the usage of prompts and identifies areas for improvement.For example, the monitoring unit monitors usage status such as the number of prompt downloads, ratings, and comments. The monitoring unit can also provide a function to visually display the usage status of prompts as graphs or charts. For example, the monitoring unit can display the usage status of prompts in chronological order, allowing usage trends to be understood. The monitoring unit can also provide a function to suggest improvements based on the usage status of prompts. For example, the monitoring unit analyzes usage data and suggests improvements to prompts. This allows the prompt sharing system according to the embodiment to efficiently share, rate, search, and monitor prompts.

[0030] The sharing unit allows users to upload prompts they have created and share them with other users. For example, the sharing unit allows users to upload their prompts in file format and allow other users to download and use the prompts. For example, the sharing unit allows users to upload their prompts as text files and allow other users to download and use the text files. The sharing unit can also provide an interface for inputting prompt metadata (e.g., prompt title, description, tags, etc.). For example, the sharing unit allows users to input prompt titles and descriptions, making it easier for other users to understand the contents of the prompt. Furthermore, the sharing unit can provide a version management function for prompts. For example, the sharing unit allows users to upload new versions of prompts and allow other users to compare them with previous versions. This allows users to easily share their prompts.

[0031] The evaluation unit allows users to evaluate the shared prompts and assess their quality. The evaluation unit, for example, provides an evaluation form that allows users to evaluate the usefulness and accuracy of the prompt. For example, the evaluation unit provides a form that allows users to evaluate the usefulness of the prompt on a five-point scale. The evaluation unit can also provide a function that allows users to leave comments on the prompt. For example, the evaluation unit allows users to enter comments about areas for improvement in the prompt or their impressions of use, so that other users can refer to them. Furthermore, the evaluation unit can tally up the evaluation results of the prompts and display them in a ranking format. For example, the evaluation unit displays the most highly rated prompts in a ranking format so that other users can refer to them. This makes it possible to evaluate the quality of the prompts.

[0032] The search unit allows users to search for prompts that meet their needs. For example, the search unit provides an interface that allows users to search by entering the title, description, tags, etc. of a prompt. For example, the search unit allows users to search by entering the title of a prompt. The search unit can also provide a filtering function. For example, the search unit provides a filtering option that allows users to search for prompts for a specific generation AI. Furthermore, the search unit can display search results in a ranking format. For example, the search unit displays the most highly rated prompts at the top of the search results, allowing users to easily find prompts that meet their needs. This allows users to easily find prompts that meet their needs.

[0033] The monitoring unit can periodically evaluate the usage of the prompt and identify areas for improvement. The monitoring unit monitors usage such as the number of prompt downloads, ratings, and comments. For example, the monitoring unit periodically tallys the number of prompt downloads to understand the usage status. The monitoring unit can also provide a function to visually display the usage status of the prompt as a graph or chart. For example, the monitoring unit can display the usage status of the prompt in chronological order to understand usage trends. Furthermore, the monitoring unit can also provide a function to suggest areas for improvement based on the usage status of the prompt. For example, the monitoring unit analyzes usage data and suggests areas for improvement of the prompt. This makes it possible to continuously evaluate the usage status of the prompt and identify areas for improvement.

[0034] The evaluation unit can evaluate the usefulness and accuracy of the prompt. For example, the evaluation unit provides an evaluation form that allows the user to evaluate the usefulness and accuracy of the prompt. For example, the evaluation unit provides a form that allows the user to evaluate the usefulness of the prompt on a five-point scale. The evaluation unit can also provide a function that allows the user to leave comments on the prompt. For example, the evaluation unit can allow the user to input comments about areas for improvement in the prompt or their usability, so that other users can refer to them. Furthermore, the evaluation unit can tally up the evaluation results of the prompts and display them in a ranking format. For example, the evaluation unit can display the most highly rated prompts in a ranking format so that other users can refer to them. This makes it possible to evaluate the usefulness and accuracy of the prompts.

[0035] The search unit can search for prompts for a specific generation AI. The search unit, for example, provides a filtering option that allows a user to search for prompts for a specific generation AI. For example, the search unit provides a filtering option that allows a user to search for prompts for a specific generation AI (e.g., GPT-4 (registered trademark), Gemini, etc.). The search unit can also display search results in a ranking format. For example, the search unit displays the most highly rated prompts at the top of the search results, allowing a user to easily find a prompt that suits their purpose. This allows a user to search for a prompt for a specific generation AI.

[0036] The monitoring unit can monitor which prompts are frequently used. The monitoring unit monitors usage status, such as the number of prompt downloads, the number of ratings, and the number of comments. For example, the monitoring unit periodically tallys the number of prompt downloads to understand the usage status. The monitoring unit can also provide a function to visually display the usage status of prompts as graphs or charts. For example, the monitoring unit can display the usage status of prompts in chronological order to understand usage trends. Furthermore, the monitoring unit can also provide a function to suggest improvements based on the usage status of prompts. For example, the monitoring unit analyzes usage status data and suggests improvements to prompts. This makes it possible to monitor frequently used prompts.

[0037] The sharing unit can analyze the user's past sharing history and select an appropriate sharing method when sharing a prompt. For example, when sharing a prompt, the sharing unit analyzes the user's past sharing history and selects the optimal sharing method. For example, the sharing unit can suggest a similar method based on a sharing method that the user has used successfully in the past. The sharing unit can also avoid a sharing method that the user has used unsuccessfully in the past and suggest an alternative method. Furthermore, the sharing unit can select and suggest the most effective sharing method from the user's past sharing history. This makes it possible to select the optimal sharing method based on the user's past sharing history.

[0038] The sharing unit can filter prompts based on the user's current project or area of ​​interest when sharing prompts. For example, the sharing unit preferentially shares prompts related to the project the user is currently working on. The sharing unit can also filter and share highly relevant prompts based on the user's area of ​​interest. Furthermore, the sharing unit can suggest related prompts based on the user's past project history. This makes it possible to filter prompts based on the user's current project or area of ​​interest.

[0039] The sharing unit can prioritize sharing highly relevant prompts by taking into account the user's geographical location information when sharing prompts. For example, the sharing unit prioritizes sharing highly relevant prompts by taking into account the user's geographical location information when sharing prompts. For example, the sharing unit prioritizes sharing prompts related to the user's current location. The sharing unit can also suggest region-specific prompts based on the user's geographical location information. Furthermore, the sharing unit can prioritize displaying prompts shared by nearby users by referring to the user's location information. This makes it possible to share highly relevant prompts by taking into account the user's geographical location information.

[0040] The sharing unit can analyze the user's social media activity and share a relevant prompt when sharing a prompt. For example, the sharing unit can analyze the user's social media activity and share a relevant prompt when sharing a prompt. For example, the sharing unit can suggest a relevant prompt based on the user's social media activity. The sharing unit can also share a relevant prompt based on the activity of accounts the user follows. Furthermore, the sharing unit can analyze the user's social media posts and suggest interesting prompts. This makes it possible to share a relevant prompt based on the user's social media activity.

[0041] The evaluation unit can adjust the level of detail of the evaluation based on the importance of the prompt during evaluation. For example, the evaluation unit adjusts the level of detail of the evaluation based on the importance of the prompt during evaluation. For example, the evaluation unit provides detailed evaluation items for an important prompt. The evaluation unit can also provide brief evaluation items for a general prompt. Furthermore, the evaluation unit can automatically adjust the level of detail of the evaluation depending on the importance of the prompt. This makes it possible to adjust the level of detail of the evaluation depending on the importance of the prompt.

[0042] The evaluation unit can apply different evaluation algorithms depending on the category of the prompt during evaluation. For example, the evaluation unit applies different evaluation algorithms depending on the category of the prompt during evaluation. For example, the evaluation unit applies technical evaluation criteria to technical prompts. The evaluation unit can also apply evaluation criteria that emphasize creativity to creative prompts. Furthermore, the evaluation unit can apply evaluation criteria that emphasize practicality to business prompts. This makes it possible to apply an appropriate evaluation algorithm depending on the category of the prompt.

[0043] The evaluation unit can determine the priority of the evaluation based on the time of submission of the prompt during evaluation. For example, the evaluation unit determines the priority of the evaluation based on the time of submission of the prompt during evaluation. For example, the evaluation unit prioritizes the evaluation of a prompt that was submitted recently. The evaluation unit can also postpone the evaluation of a prompt that was submitted earlier. Furthermore, the evaluation unit can automatically adjust the priority of the evaluation based on the time of submission. This makes it possible to determine the priority of the evaluation based on the time of submission of the prompt.

[0044] The evaluation unit can adjust the order of evaluation based on the relevance of the prompts during evaluation. For example, the evaluation unit adjusts the order of evaluation based on the relevance of the prompts during evaluation. For example, the evaluation unit prioritizes evaluation of highly relevant prompts. The evaluation unit can also postpone evaluation of less relevant prompts. Furthermore, the evaluation unit can automatically adjust the order of evaluation based on the relevance of the prompts. This makes it possible to adjust the order of evaluation based on the relevance of the prompts.

[0045] The search unit can improve the accuracy of a search by taking into account the interrelationships between prompts during a search. For example, the search unit can improve the accuracy of a search by taking into account the interrelationships between prompts during a search. For example, the search unit can group and display related prompts. The search unit can also preferentially display highly relevant prompts based on the interrelationships between prompts. Furthermore, the search unit can improve the accuracy of search results by taking into account the interrelationships between prompts. This makes it possible to improve the accuracy of a search by taking into account the interrelationships between prompts.

[0046] The search unit can perform a search taking into account attribute information of the prompt submitter when performing a search. For example, the search unit can perform a search taking into account attribute information of the prompt submitter when performing a search. For example, the search unit can preferentially display relevant prompts based on the submitter's field of expertise. The search unit can also preferentially display highly reliable prompts based on the submitter's evaluation history. Furthermore, the search unit can improve the accuracy of search results by taking into account attribute information of the submitter. This allows a search to be performed taking into account attribute information of the prompt submitter.

[0047] The search unit can perform a search taking into account the geographical distribution of prompts. For example, the search unit performs a search taking into account the geographical distribution of prompts. For example, the search unit preferentially displays prompts related to the user's current location. The search unit can also display region-specific prompts based on the geographical distribution of prompts. Furthermore, the search unit can improve the accuracy of search results by taking into account the geographical distribution of prompts. This allows a search to be performed taking into account the geographical distribution of prompts.

[0048] The search unit can improve the accuracy of the search by referring to literature related to the prompt during a search. The search unit can improve the accuracy of the search by, for example, referring to literature related to the prompt during a search. For example, the search unit can preferentially display highly relevant prompts based on the related literature. The search unit can also improve the accuracy of search results by referring to the related literature. Furthermore, the search unit can evaluate the reliability of the prompt based on the related literature. This makes it possible to improve the accuracy of the search by referring to literature related to the prompt.

[0049] The monitoring unit can predict the current usage status by referring to past monitoring data during monitoring. For example, the monitoring unit predicts the current usage status by referring to past monitoring data during monitoring. For example, the monitoring unit predicts the current usage status based on the past usage data. The monitoring unit can also predict changes in the usage status by referring to the past monitoring data. Furthermore, the monitoring unit can also predict future usage status based on the past data. This makes it possible to predict the current usage status based on the past monitoring data.

[0050] The monitoring unit can apply different monitoring methods to each category of prompt during monitoring. For example, the monitoring unit applies different monitoring methods to each category of prompt during monitoring. For example, the monitoring unit applies a technical monitoring method to a technical prompt. The monitoring unit can also apply a monitoring method that emphasizes creativity to a creative prompt. Furthermore, the monitoring unit can apply a monitoring method that emphasizes practicality to a business prompt. This makes it possible to apply an appropriate monitoring method to each category of prompt.

[0051] The monitoring unit can analyze changes in usage status based on the submission time of the prompt during monitoring. The monitoring unit can, for example, analyze changes in usage status based on the submission time of the prompt during monitoring. For example, the monitoring unit prioritizes analyzing the usage status of prompts that were submitted more recently. The monitoring unit can also postpone analyzing the usage status of prompts that were submitted earlier. Furthermore, the monitoring unit can automatically analyze changes in usage status based on the submission time. This makes it possible to analyze changes in usage status based on the submission time of the prompt.

[0052] The monitoring unit can analyze usage status by referring to market data related to the prompt during monitoring. For example, the monitoring unit analyzes usage status by referring to market data related to the prompt during monitoring. For example, the monitoring unit analyzes usage status of the prompt based on the related market data. The monitoring unit can also evaluate the popularity of the prompt by referring to market data. Furthermore, the monitoring unit can evaluate the usefulness of the prompt based on the related market data. In this way, usage status can be analyzed based on market data related to the prompt.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The prompt sharing system may further include a "notification unit." The notification unit allows a user to receive notifications when a new prompt is shared. For example, the notification unit may send a notification when a prompt of a particular category that the user is interested in is shared. The notification unit may also send a notification when another user that the user follows shares a prompt. The notification unit may also send a notification when feedback is received on a prompt that the user has rated. This allows the user to quickly become aware of new prompts and feedback.

[0055] The prompt sharing system may further include a "recommendation unit." The recommendation unit recommends prompts that are appropriate for the user based on the user's past usage history and rating history. For example, the recommendation unit may recommend prompts similar to prompts that the user has given high ratings to in the past. The recommendation unit may also preferentially recommend prompts in categories that the user frequently uses. Furthermore, the recommendation unit may recommend prompts that other users have given high ratings to. This allows users to easily find prompts that are appropriate for them.

[0056] The prompt sharing system may further include a "translation unit." The translation unit provides a function for translating shared prompts into different languages. For example, the translation unit translates a prompt shared in English into Japanese. The translation unit may also translate prompts into a language selected by the user. Furthermore, the translation unit may evaluate the quality of the translated prompt and provide the user with the optimal translation. This makes it easier for users who speak different languages ​​to share prompts.

[0057] The prompt sharing system may further include a "history unit." The history unit stores and allows a user to refer to a history of prompts that the user has used in the past. For example, the history unit may display a list of prompts that the user has downloaded in the past. The history unit may also display a history of prompts that the user has rated in the past. Furthermore, the history unit may display a history of prompts on which the user has left comments in the past. This allows a user to easily refer to prompts that they have used in the past.

[0058] The prompt sharing system can further include an "analysis unit." The analysis unit performs detailed analysis of the usage of shared prompts and provides feedback to users. For example, the analysis unit analyzes the frequency of prompt usage and evaluation trends. The analysis unit can also analyze attribute information of prompt users to understand what types of users are using prompts. Furthermore, the analysis unit can provide data to suggest improvements to prompts. This can be useful for improving the quality of prompts.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The sharing unit allows users to upload their own created prompts and share them with other users. For example, the sharing unit allows users to upload their own prompts in file format and make the prompts available for download. The sharing unit can also provide an interface for entering prompt metadata (e.g., prompt title, description, tags, etc.). Furthermore, the sharing unit can provide a version management function for prompts. Step 2: The evaluator evaluates the prompts shared by users and evaluates their quality. For example, the evaluator provides an evaluation form for users to evaluate the usefulness and accuracy of the prompts. The evaluator can also provide a function that allows users to leave comments on the prompts. Furthermore, the evaluator can compile the evaluation results of the prompts and display them in a ranking format. Step 3: The search unit allows the user to search for a prompt that meets their needs. For example, the search unit provides an interface that allows the user to search by entering the title, description, tags, etc. of the prompt. The search unit can also provide a filtering function. Furthermore, the search unit can display the search results in a ranking format. Step 4: The monitoring unit continuously evaluates the usage of the prompt and identifies areas for improvement. For example, the monitoring unit monitors usage such as the number of prompt downloads, ratings, and comments. The monitoring unit can also provide a function that visually displays the usage of the prompt as graphs or charts. Furthermore, the monitoring unit can provide a function that suggests areas for improvement based on the usage of the prompt.

[0061] (Example 2) The prompt sharing system according to an embodiment of the present invention is a system that open-sources prompts for use with a generative AI and provides a community-based service. This prompt sharing system provides value to both prompt creators and users. Specifically, it includes a "sharing section" for sharing prompts, allowing users to upload their own prompts and share them with other users. It also includes an "evaluation section" for rating prompts, allowing users to rate shared prompts and evaluate their quality. It also includes a "search section" for searching prompts, allowing users to easily find prompts that meet their needs. Finally, it includes a "monitoring section" for monitoring the usage of prompts, allowing users to continuously evaluate and improve the usability of prompts. This system allows prompt creators to share their prompts and receive evaluations to identify areas for improvement, and users can easily find prompts that meet their needs and achieve the output they desire. For example, the prompt sharing system allows users to upload prompts they create and share them with other users. For example, by sharing a specific prompt for a generative AI, other users can use that prompt. Next, the prompt sharing system allows users to evaluate the shared prompts and evaluate their quality. For example, by rating the prompt's usefulness and accuracy, other users can use the prompts as a reference. Furthermore, the prompt sharing system makes it easy for users to find prompts that suit their purposes. For example, they can find prompts that suit their purposes by searching for prompts for a specific generation AI. Finally, the prompt sharing system can continuously evaluate the usage of prompts and identify areas for improvement. For example, it can identify popular prompts by monitoring which prompts are used most frequently. This mechanism allows prompt creators to share their prompts and receive evaluations to identify areas for improvement, and users can easily find prompts that suit their purposes and obtain the output they expect.This allows the prompt sharing system to efficiently share, evaluate, search, and monitor prompts.

[0062] A prompt sharing system according to an embodiment includes a sharing unit, an evaluation unit, a search unit, and a monitoring unit. The sharing unit allows users to upload prompts they have created and share them with other users. For example, the sharing unit allows users to upload their own prompts in file format, allowing other users to download and use the prompts. The sharing unit can also provide an interface for inputting prompt metadata (e.g., prompt title, description, tags, etc.). For example, the sharing unit allows users to input prompt titles and descriptions, making it easier for other users to understand the content of the prompt. The sharing unit can also provide a version management function for prompts. For example, the sharing unit allows users to upload new versions of prompts and allow other users to compare them with previous versions. The evaluation unit allows users to evaluate shared prompts and evaluate their quality. For example, the evaluation unit provides an evaluation form that allows users to evaluate the usefulness and accuracy of prompts. The evaluation unit can also provide a function that allows users to leave comments on prompts. For example, the evaluation unit allows users to input comments about improvements to the prompt or their usability, allowing other users to refer to the comments. The evaluation unit can also compile the prompt evaluation results and display them in a ranking format. For example, the evaluation unit displays the most highly rated prompts in a ranked format so that other users can refer to them. The search unit allows users to search for prompts that suit their purposes. For example, the search unit provides an interface that allows users to search by entering the title, description, tags, etc. of a prompt. The search unit can also provide a filtering function. For example, the search unit provides filtering options that allow users to search for prompts for a specific generation AI. Furthermore, the search unit can display search results in a ranked format. For example, the search unit displays the most highly rated prompts at the top of the search results so that users can easily find prompts that suit their purposes. The monitoring unit continuously evaluates the usage of prompts and identifies areas for improvement.For example, the monitoring unit monitors usage status such as the number of prompt downloads, ratings, and comments. The monitoring unit can also provide a function to visually display the usage status of prompts as graphs or charts. For example, the monitoring unit can display the usage status of prompts in chronological order, allowing usage trends to be understood. The monitoring unit can also provide a function to suggest improvements based on the usage status of prompts. For example, the monitoring unit analyzes usage data and suggests improvements to prompts. This allows the prompt sharing system according to the embodiment to efficiently share, rate, search, and monitor prompts.

[0063] The sharing unit allows users to upload prompts they have created and share them with other users. For example, the sharing unit allows users to upload their prompts in file format and allow other users to download and use the prompts. For example, the sharing unit allows users to upload their prompts as text files and allow other users to download and use the text files. The sharing unit can also provide an interface for inputting prompt metadata (e.g., prompt title, description, tags, etc.). For example, the sharing unit allows users to input prompt titles and descriptions, making it easier for other users to understand the contents of the prompt. Furthermore, the sharing unit can provide a version management function for prompts. For example, the sharing unit allows users to upload new versions of prompts and allow other users to compare them with previous versions. This allows users to easily share their prompts.

[0064] The evaluation unit allows users to evaluate the shared prompts and assess their quality. The evaluation unit, for example, provides an evaluation form that allows users to evaluate the usefulness and accuracy of the prompt. For example, the evaluation unit provides a form that allows users to evaluate the usefulness of the prompt on a five-point scale. The evaluation unit can also provide a function that allows users to leave comments on the prompt. For example, the evaluation unit allows users to enter comments about areas for improvement in the prompt or their impressions of use, so that other users can refer to them. Furthermore, the evaluation unit can tally up the evaluation results of the prompts and display them in a ranking format. For example, the evaluation unit displays the most highly rated prompts in a ranking format so that other users can refer to them. This makes it possible to evaluate the quality of the prompts.

[0065] The search unit allows users to search for prompts that meet their needs. For example, the search unit provides an interface that allows users to search by entering the title, description, tags, etc. of a prompt. For example, the search unit allows users to search by entering the title of a prompt. The search unit can also provide a filtering function. For example, the search unit provides a filtering option that allows users to search for prompts for a specific generation AI. Furthermore, the search unit can display search results in a ranking format. For example, the search unit displays the most highly rated prompts at the top of the search results, allowing users to easily find prompts that meet their needs. This allows users to easily find prompts that meet their needs.

[0066] The monitoring unit can periodically evaluate the usage of the prompt and identify areas for improvement. The monitoring unit monitors usage such as the number of prompt downloads, ratings, and comments. For example, the monitoring unit periodically tallys the number of prompt downloads to understand the usage status. The monitoring unit can also provide a function to visually display the usage status of the prompt as a graph or chart. For example, the monitoring unit can display the usage status of the prompt in chronological order to understand usage trends. Furthermore, the monitoring unit can also provide a function to suggest areas for improvement based on the usage status of the prompt. For example, the monitoring unit analyzes usage data and suggests areas for improvement of the prompt. This makes it possible to continuously evaluate the usage status of the prompt and identify areas for improvement.

[0067] The evaluation unit can evaluate the usefulness and accuracy of the prompt. For example, the evaluation unit provides an evaluation form that allows the user to evaluate the usefulness and accuracy of the prompt. For example, the evaluation unit provides a form that allows the user to evaluate the usefulness of the prompt on a five-point scale. The evaluation unit can also provide a function that allows the user to leave comments on the prompt. For example, the evaluation unit can allow the user to input comments about areas for improvement in the prompt or their usability, so that other users can refer to them. Furthermore, the evaluation unit can tally up the evaluation results of the prompts and display them in a ranking format. For example, the evaluation unit can display the most highly rated prompts in a ranking format so that other users can refer to them. This makes it possible to evaluate the usefulness and accuracy of the prompts.

[0068] The search unit can search for prompts for a specific generation AI. The search unit, for example, provides a filtering option that allows a user to search for prompts for a specific generation AI. For example, the search unit provides a filtering option that allows a user to search for prompts for a specific generation AI (e.g., GPT-4, Gemini, etc.). The search unit can also display search results in a ranking format. For example, the search unit displays the most highly rated prompts at the top of the search results, allowing a user to easily find a prompt that suits their purpose. This makes it possible to search for prompts for a specific generation AI.

[0069] The monitoring unit can monitor which prompts are frequently used. The monitoring unit monitors usage status, such as the number of prompt downloads, the number of ratings, and the number of comments. For example, the monitoring unit periodically tallys the number of prompt downloads to understand the usage status. The monitoring unit can also provide a function to visually display the usage status of prompts as graphs or charts. For example, the monitoring unit can display the usage status of prompts in chronological order to understand usage trends. Furthermore, the monitoring unit can also provide a function to suggest improvements based on the usage status of prompts. For example, the monitoring unit analyzes usage status data and suggests improvements to prompts. This makes it possible to monitor frequently used prompts.

[0070] The sharing unit can estimate the user's emotions and adjust the timing of sharing the prompt based on the estimated user emotions. The sharing unit, for example, estimates the user's emotions and adjusts the timing of sharing the prompt based on the estimated emotions. For example, if the user is excited, the sharing unit prompts the user to share the prompt immediately. Also, if the user is relaxed, the sharing unit can suggest sharing the prompt at an appropriate time. Furthermore, if the user is feeling stressed, the sharing unit can delay the timing of sharing and prompt the user to share when they have calmed down. This makes it possible to adjust the timing of sharing the prompt according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0071] The sharing unit can analyze the user's past sharing history and select an appropriate sharing method when sharing a prompt. For example, when sharing a prompt, the sharing unit analyzes the user's past sharing history and selects the optimal sharing method. For example, the sharing unit can suggest a similar method based on a sharing method that the user has used successfully in the past. The sharing unit can also avoid a sharing method that the user has used unsuccessfully in the past and suggest an alternative method. Furthermore, the sharing unit can select and suggest the most effective sharing method from the user's past sharing history. This makes it possible to select the optimal sharing method based on the user's past sharing history.

[0072] The sharing unit can filter prompts based on the user's current project or area of ​​interest when sharing prompts. For example, the sharing unit preferentially shares prompts related to the project the user is currently working on. The sharing unit can also filter and share highly relevant prompts based on the user's area of ​​interest. Furthermore, the sharing unit can suggest related prompts based on the user's past project history. This makes it possible to filter prompts based on the user's current project or area of ​​interest.

[0073] The sharing unit can estimate the user's emotions and determine the priority of prompts to be shared based on the estimated user emotions. The sharing unit, for example, estimates the user's emotions and determines the priority of prompts to be shared based on the estimated emotions. For example, if the user is excited, the sharing unit can prioritize sharing important prompts. Also, if the user is relaxed, the sharing unit can prioritize sharing interesting prompts. Furthermore, if the user is stressed, the sharing unit can prioritize sharing simple and easy-to-understand prompts. In this way, the priority of prompts to be shared can be determined according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0074] The sharing unit can prioritize sharing highly relevant prompts by taking into account the user's geographical location information when sharing prompts. For example, the sharing unit prioritizes sharing highly relevant prompts by taking into account the user's geographical location information when sharing prompts. For example, the sharing unit prioritizes sharing prompts related to the user's current location. The sharing unit can also suggest region-specific prompts based on the user's geographical location information. Furthermore, the sharing unit can prioritize displaying prompts shared by nearby users by referring to the user's location information. This makes it possible to share highly relevant prompts by taking into account the user's geographical location information.

[0075] The sharing unit can analyze the user's social media activity and share a relevant prompt when sharing a prompt. For example, the sharing unit can analyze the user's social media activity and share a relevant prompt when sharing a prompt. For example, the sharing unit can suggest a relevant prompt based on the user's social media activity. The sharing unit can also share a relevant prompt based on the activity of accounts the user follows. Furthermore, the sharing unit can analyze the user's social media posts and suggest interesting prompts. This makes it possible to share a relevant prompt based on the user's social media activity.

[0076] The evaluation unit can estimate the user's emotion and adjust the way the evaluation is expressed based on the estimated user emotion. For example, the evaluation unit can estimate the user's emotion and adjust the way the evaluation is expressed based on the estimated emotion. For example, if the user is relaxed, the evaluation unit can request detailed evaluation comments. If the user is in a hurry, the evaluation unit can also provide a concise evaluation method. Furthermore, if the user is excited, the evaluation unit can also provide a visually attractive evaluation interface. This makes it possible to adjust the way the evaluation is expressed depending on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0077] The evaluation unit can adjust the level of detail of the evaluation based on the importance of the prompt during evaluation. For example, the evaluation unit adjusts the level of detail of the evaluation based on the importance of the prompt during evaluation. For example, the evaluation unit provides detailed evaluation items for an important prompt. The evaluation unit can also provide brief evaluation items for a general prompt. Furthermore, the evaluation unit can automatically adjust the level of detail of the evaluation depending on the importance of the prompt. This makes it possible to adjust the level of detail of the evaluation depending on the importance of the prompt.

[0078] The evaluation unit can apply different evaluation algorithms depending on the category of the prompt during evaluation. For example, the evaluation unit applies different evaluation algorithms depending on the category of the prompt during evaluation. For example, the evaluation unit applies technical evaluation criteria to technical prompts. The evaluation unit can also apply evaluation criteria that emphasize creativity to creative prompts. Furthermore, the evaluation unit can apply evaluation criteria that emphasize practicality to business prompts. This makes it possible to apply an appropriate evaluation algorithm depending on the category of the prompt.

[0079] The evaluation unit can estimate the user's emotion and adjust the length of the evaluation based on the estimated user emotion. The evaluation unit, for example, estimates the user's emotion and adjusts the length of the evaluation based on the estimated emotion. For example, the evaluation unit requests a detailed evaluation when the user is relaxed. The evaluation unit can also request a short evaluation when the user is in a hurry. Furthermore, the evaluation unit can provide a visually appealing evaluation interface when the user is excited. This allows the length of the evaluation to be adjusted according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0080] The evaluation unit can determine the priority of the evaluation based on the time of submission of the prompt during evaluation. For example, the evaluation unit determines the priority of the evaluation based on the time of submission of the prompt during evaluation. For example, the evaluation unit prioritizes the evaluation of a prompt that was submitted recently. The evaluation unit can also postpone the evaluation of a prompt that was submitted earlier. Furthermore, the evaluation unit can automatically adjust the priority of the evaluation based on the time of submission. This makes it possible to determine the priority of the evaluation based on the time of submission of the prompt.

[0081] The evaluation unit can adjust the order of evaluation based on the relevance of the prompts during evaluation. For example, the evaluation unit adjusts the order of evaluation based on the relevance of the prompts during evaluation. For example, the evaluation unit prioritizes evaluation of highly relevant prompts. The evaluation unit can also postpone evaluation of less relevant prompts. Furthermore, the evaluation unit can automatically adjust the order of evaluation based on the relevance of the prompts. This makes it possible to adjust the order of evaluation based on the relevance of the prompts.

[0082] The search unit can estimate the user's emotion and adjust the display method of the search results based on the estimated user emotion. The search unit, for example, estimates the user's emotion and adjusts the display method of the search results based on the estimated emotion. For example, the search unit displays detailed search results when the user is relaxed. The search unit can also display concise search results when the user is in a hurry. Furthermore, the search unit can display visually appealing search results when the user is excited. This makes it possible to adjust the display method of the search results according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0083] The search unit can improve the accuracy of a search by taking into account the interrelationships between prompts during a search. For example, the search unit can improve the accuracy of a search by taking into account the interrelationships between prompts during a search. For example, the search unit can group and display related prompts. The search unit can also preferentially display highly relevant prompts based on the interrelationships between prompts. Furthermore, the search unit can improve the accuracy of search results by taking into account the interrelationships between prompts. This makes it possible to improve the accuracy of a search by taking into account the interrelationships between prompts.

[0084] The search unit can perform a search taking into account attribute information of the prompt submitter when performing a search. For example, the search unit can perform a search taking into account attribute information of the prompt submitter when performing a search. For example, the search unit can preferentially display relevant prompts based on the submitter's field of expertise. The search unit can also preferentially display highly reliable prompts based on the submitter's evaluation history. Furthermore, the search unit can improve the accuracy of search results by taking into account attribute information of the submitter. This allows a search to be performed taking into account attribute information of the prompt submitter.

[0085] The search unit can estimate the user's emotion and adjust the display order of search results based on the estimated user emotion. The search unit, for example, estimates the user's emotion and adjusts the display order of search results based on the estimated emotion. For example, the search unit can prioritize displaying detailed search results when the user is relaxed. The search unit can also prioritize displaying concise search results when the user is in a hurry. Furthermore, the search unit can prioritize displaying visually appealing search results when the user is excited. This makes it possible to adjust the display order of search results according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0086] The search unit can perform a search taking into account the geographical distribution of prompts. For example, the search unit performs a search taking into account the geographical distribution of prompts. For example, the search unit preferentially displays prompts related to the user's current location. The search unit can also display region-specific prompts based on the geographical distribution of prompts. Furthermore, the search unit can improve the accuracy of search results by taking into account the geographical distribution of prompts. This allows a search to be performed taking into account the geographical distribution of prompts.

[0087] The search unit can improve the accuracy of the search by referring to literature related to the prompt during a search. The search unit can improve the accuracy of the search by, for example, referring to literature related to the prompt during a search. For example, the search unit can preferentially display highly relevant prompts based on the related literature. The search unit can also improve the accuracy of search results by referring to the related literature. Furthermore, the search unit can evaluate the reliability of the prompt based on the related literature. This makes it possible to improve the accuracy of the search by referring to literature related to the prompt.

[0088] The monitoring unit can estimate the user's emotion and adjust the monitoring display method based on the estimated user's emotion. The monitoring unit, for example, estimates the user's emotion and adjusts the monitoring display method based on the estimated emotion. For example, the monitoring unit can display detailed monitoring information when the user is relaxed. Also, the monitoring unit can display concise monitoring information when the user is in a hurry. Furthermore, the monitoring unit can display visually appealing monitoring information when the user is excited. This makes it possible to adjust the monitoring display method according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0089] The monitoring unit can predict the current usage status by referring to past monitoring data during monitoring. For example, the monitoring unit predicts the current usage status by referring to past monitoring data during monitoring. For example, the monitoring unit predicts the current usage status based on the past usage data. The monitoring unit can also predict changes in the usage status by referring to the past monitoring data. Furthermore, the monitoring unit can also predict future usage status based on the past data. This makes it possible to predict the current usage status based on the past monitoring data.

[0090] The monitoring unit can apply different monitoring methods to each category of prompt during monitoring. For example, the monitoring unit applies different monitoring methods to each category of prompt during monitoring. For example, the monitoring unit applies a technical monitoring method to a technical prompt. The monitoring unit can also apply a monitoring method that emphasizes creativity to a creative prompt. Furthermore, the monitoring unit can apply a monitoring method that emphasizes practicality to a business prompt. This makes it possible to apply an appropriate monitoring method to each category of prompt.

[0091] The monitoring unit can estimate the user's emotion and adjust the importance of monitoring based on the estimated user's emotion. For example, the monitoring unit can estimate the user's emotion and adjust the importance of monitoring based on the estimated emotion. For example, the monitoring unit can provide detailed monitoring information when the user is relaxed. The monitoring unit can also provide concise monitoring information when the user is in a hurry. Furthermore, the monitoring unit can also provide visually appealing monitoring information when the user is excited. This makes it possible to adjust the importance of monitoring according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0092] The monitoring unit can analyze changes in usage status based on the submission time of the prompt during monitoring. The monitoring unit can, for example, analyze changes in usage status based on the submission time of the prompt during monitoring. For example, the monitoring unit prioritizes analyzing the usage status of prompts that were submitted more recently. The monitoring unit can also postpone analyzing the usage status of prompts that were submitted earlier. Furthermore, the monitoring unit can automatically analyze changes in usage status based on the submission time. This makes it possible to analyze changes in usage status based on the submission time of the prompt.

[0093] The monitoring unit can analyze usage status by referring to market data related to the prompt during monitoring. For example, the monitoring unit analyzes usage status by referring to market data related to the prompt during monitoring. For example, the monitoring unit analyzes usage status of the prompt based on the related market data. The monitoring unit can also evaluate the popularity of the prompt by referring to market data. Furthermore, the monitoring unit can evaluate the usefulness of the prompt based on the related market data. In this way, usage status can be analyzed based on market data related to the prompt. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned sharing unit, evaluation unit, search unit, and monitoring unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the sharing unit is realized by the control unit 46A of the smart device 14, allowing a user to upload prompts and share them with other users. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12, allowing a user to evaluate shared prompts. The search unit is realized by the control unit 46A of the smart device 14, allowing a user to search for prompts that suit their purpose. The monitoring unit is realized by the specific processing unit 290 of the data processing device 12, allowing a user to continuously evaluate the usage status of prompts. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned sharing unit, evaluation unit, search unit, and monitoring unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the sharing unit is realized by the control unit 46A of the smart glasses 214, allowing a user to upload prompts and share them with other users. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12, allowing a user to evaluate shared prompts. The search unit is realized by the control unit 46A of the smart glasses 214, allowing a user to search for prompts that suit their purpose. The monitoring unit is realized by the specific processing unit 290 of the data processing device 12, allowing a user to continuously evaluate the usage status of prompts. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned sharing unit, evaluation unit, search unit, and monitoring unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the sharing unit is realized by the control unit 46A of the headset type terminal 314, and allows a user to upload prompts and share them with other users. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12, and allows a user to evaluate shared prompts. The search unit is realized by the control unit 46A of the headset type terminal 314, and allows a user to search for prompts that suit their purpose. The monitoring unit is realized by the specific processing unit 290 of the data processing device 12, and can continuously evaluate the usage status of prompts. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned sharing unit, evaluation unit, search unit, and monitoring unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the sharing unit is realized by the control unit 46A of the robot 414, and allows a user to upload prompts and share them with other users. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12, and allows a user to evaluate shared prompts. The search unit is realized by the control unit 46A of the robot 414, and allows a user to search for prompts that suit their purpose. The monitoring unit is realized by the specific processing unit 290 of the data processing device 12, and can continuously evaluate the usage status of prompts.

[0094] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0095] The prompt sharing system may further include a "notification unit." The notification unit allows a user to receive notifications when a new prompt is shared. For example, the notification unit may send a notification when a prompt of a particular category that the user is interested in is shared. The notification unit may also send a notification when another user that the user follows shares a prompt. The notification unit may also send a notification when feedback is received on a prompt that the user has rated. This allows the user to quickly become aware of new prompts and feedback.

[0096] The prompt sharing system may further include a "recommendation unit." The recommendation unit recommends prompts that are appropriate for the user based on the user's past usage history and rating history. For example, the recommendation unit may recommend prompts similar to prompts that the user has given high ratings to in the past. The recommendation unit may also preferentially recommend prompts in categories that the user frequently uses. Furthermore, the recommendation unit may recommend prompts that other users have given high ratings to. This allows users to easily find prompts that are appropriate for them.

[0097] The prompt sharing system may further include a "translation unit." The translation unit provides a function for translating shared prompts into different languages. For example, the translation unit translates a prompt shared in English into Japanese. The translation unit may also translate prompts into a language selected by the user. Furthermore, the translation unit may evaluate the quality of the translated prompt and provide the user with the optimal translation. This makes it easier for users who speak different languages ​​to share prompts.

[0098] The prompt sharing system may further include a "history unit." The history unit stores and allows a user to refer to a history of prompts that the user has used in the past. For example, the history unit may display a list of prompts that the user has downloaded in the past. The history unit may also display a history of prompts that the user has rated in the past. Furthermore, the history unit may display a history of prompts on which the user has left comments in the past. This allows a user to easily refer to prompts that they have used in the past.

[0099] The prompt sharing system can further include an "analysis unit." The analysis unit performs detailed analysis of the usage of shared prompts and provides feedback to users. For example, the analysis unit analyzes the frequency of prompt usage and evaluation trends. The analysis unit can also analyze attribute information of prompt users to understand what types of users are using prompts. Furthermore, the analysis unit can provide data to suggest improvements to prompts. This can be useful for improving the quality of prompts.

[0100] The evaluation unit can estimate the user's emotion and adjust the evaluation feedback method based on the estimated user's emotion. For example, the evaluation unit can request detailed feedback when the user is relaxed. The evaluation unit can also request brief feedback when the user is in a hurry. Furthermore, the evaluation unit can provide a visually attractive feedback interface when the user is excited. This makes it possible to adjust the evaluation feedback method according to the user's emotion.

[0101] The search unit can estimate the user's emotions and adjust the display format of the search results based on the estimated user's emotions. For example, the search unit can display detailed search results when the user is relaxed. The search unit can also display concise search results when the user is in a hurry. Furthermore, the search unit can display visually appealing search results when the user is excited. In this way, the display format of the search results can be adjusted according to the user's emotions.

[0102] The monitoring unit can estimate the user's emotion and adjust the display method of the monitoring data based on the estimated user's emotion. For example, the monitoring unit can display detailed monitoring data when the user is relaxed. Also, the monitoring unit can display concise monitoring data when the user is in a hurry. Furthermore, the monitoring unit can display visually appealing monitoring data when the user is excited. In this way, the display method of the monitoring data can be adjusted according to the user's emotion.

[0103] The sharing unit can estimate the user's emotions and determine the priority of prompts to be shared based on the estimated user's emotions. For example, if the user is excited, the sharing unit can prioritize sharing important prompts. Also, if the user is relaxed, the sharing unit can prioritize sharing interesting prompts. Furthermore, if the user is stressed, the sharing unit can prioritize sharing simple and easy-to-understand prompts. In this way, the priority of prompts to be shared can be determined according to the user's emotions.

[0104] The evaluation unit can estimate the user's emotion and adjust the length of the evaluation based on the estimated user's emotion. For example, the evaluation unit can request a detailed evaluation when the user is relaxed. The evaluation unit can also request a short evaluation when the user is in a hurry. Furthermore, the evaluation unit can provide a visually attractive evaluation interface when the user is excited. This allows the length of the evaluation to be adjusted according to the user's emotion.

[0105] The processing flow of the second embodiment will be briefly explained below.

[0106] Step 1: The sharing unit allows users to upload their own created prompts and share them with other users. For example, the sharing unit allows users to upload their own prompts in file format and make the prompts available for download. The sharing unit can also provide an interface for entering prompt metadata (e.g., prompt title, description, tags, etc.). Furthermore, the sharing unit can provide a version management function for prompts. Step 2: The evaluator evaluates the prompts shared by users and evaluates their quality. For example, the evaluator provides an evaluation form for users to evaluate the usefulness and accuracy of the prompts. The evaluator can also provide a function that allows users to leave comments on the prompts. Furthermore, the evaluator can compile the evaluation results of the prompts and display them in a ranking format. Step 3: The search unit allows the user to search for a prompt that meets their needs. For example, the search unit provides an interface that allows the user to search by entering the title, description, tags, etc. of the prompt. The search unit can also provide a filtering function. Furthermore, the search unit can display the search results in a ranking format. Step 4: The monitoring unit continuously evaluates the usage of the prompt and identifies areas for improvement. For example, the monitoring unit monitors usage such as the number of prompt downloads, ratings, and comments. The monitoring unit can also provide a function that visually displays the usage of the prompt as graphs or charts. Furthermore, the monitoring unit can provide a function that suggests areas for improvement based on the usage of the prompt.

[0107] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0108] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0109] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0110] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0111] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0112] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0113] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0114] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0115] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0117] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0118] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0119] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0121] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0122] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0125] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0137] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0141] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0145] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0146] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0147] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0149] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0150] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0151] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0152] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0153] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0154] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0155] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0156] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0157] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0158] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0159] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0160] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0161] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0162] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0163] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0164] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0165] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0166] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0167] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0168] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0169] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0170] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0171] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0172] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0173] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0174] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0175] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0176] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0177] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0178] [Explanation of symbols]

[0179] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A sharing section for sharing prompts, an evaluation unit that evaluates the prompt shared by the sharing unit; a search unit that searches for prompts evaluated by the evaluation unit; a monitoring unit that monitors the usage status of the prompts searched by the search unit; Equipped with A system characterized by:

2. The common part is Users can upload their own prompts and share them with others. The system of claim 1 .

3. The evaluation unit Users rate the shared prompts and evaluate their quality The system of claim 1 .

4. The search unit Users search for the appropriate prompt The system of claim 1 .

5. The monitoring unit Regularly evaluate prompt usage and identify areas for improvement The system of claim 1 .

6. The evaluation unit Evaluate the prompts for their usefulness and accuracy The system of claim 1 .

7. The search unit Search for prompts for a specific generated AI The system of claim 1 .

8. The monitoring unit Monitor which prompts are most popular The system of claim 1 .

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